What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
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Taught By

Polong Lin

Data Scientist

Transcript

Welcome. In this video, we'll introduce you to Jupyter Notebooks. Jupyter Notebooks are like documents, where you can execute chunks of programming code, one chunk at a time. You can do everything from creating interactive maps, to creating interesting data visualizations, and even embedding videos. Jupyter Notebooks are open-source and was designed for interactive data science in scientific computing. Data scientists use Jupyter Notebooks, because in data science, you're often exploring your data or building models, and needing to see the outputs of parts of your code quite frequently, which Jupyter Notebooks enables. Jupyter Notebooks were also designed to be shared with others. You can tell stories in Jupyter Notebooks with your data by combining your code with explanatory text, output from your code, images, and videos.

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